利用模糊逻辑、动力系统和分形理论进行金融时间序列预测的自动数学建模

O. Castillo, P. Melin
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引用次数: 39

摘要

我们描述了一种利用模糊逻辑技术、动力系统和分形理论对金融时间序列预测进行自动数学建模的新方法。主要思想是使用模糊逻辑技术,我们可以模拟和自动化人类专家在金融时间序列预测数学建模中的推理过程。我们的自动化建模新方法包括三个主要部分:时间序列分析,开发一组可接受的模型,以及选择“最佳”模型。我们的时间序列分析方法包括使用一组点的分形维数作为时间序列几何复杂性的度量。我们开发一套可容许的动态系统模型的方法是基于使用模糊逻辑技术来模拟人类专家在建模金融问题时的决策过程。金融时间序列预测(FTSP)的“最佳”模型的选择是利用专家的启发式和统计计算来完成的。该方法可作为计算机程序实现,可视为FTSP自动化数学建模的智能系统。
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Automated mathematical modelling for financial time series prediction using fuzzy logic, dynamical systems and fractal theory
We describe a new method for performing automated mathematical modelling for financial time series prediction using fuzzy logic techniques, dynamical systems and fractal theory. The main idea is that using fuzzy logic techniques we can simulate and automate the reasoning process of human experts in mathematical modelling for financial time series prediction. Our new method for automated modelling consists of three main parts: time series analysis, developing a set of admissible models, and selecting the "best" model. Our method for time series analysis consists of using the fractal dimension of a set of points as a measure of the geometrical complexity of the time series. Our method for developing a set of admissible dynamical systems models is based on the use of fuzzy logic techniques to simulate the decision process of the human experts in modelling financial problems. The selection of the "best" model for financial time series prediction (FTSP) is done using heuristics from the experts and statistical calculations. This new method can be implemented as a computer program and can be considered an intelligent system for automated mathematical modelling for FTSP.
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